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There is a common perception that the prices of unrelated commodities move together. This paper re-examines this notion, using a measure of comovement of economic time series called concordance. Concordance measures the proportion of time that the prices of two commodities are concurrently in the same boom period or same slump period. Using data on the prices of several unrelated commodities, the paper finds no evidence of comovement in commodity prices. The results carry an important policy implication, as the study provides no support for earlier claims of irrational trading behavior by participants in world commodity markets.
There is a common perception that the prices of unrelated commodities move together. This paper re-examines this notion, using a measure of co-movement of economic time series called concordance. Concordance measures the proportion of time that the prices of two commodities are concurrently in the same boom period or same slump period. Using data on the prices of several unrelated commodities, the paper finds no evidence of co-movement in commodity prices. The results carry an important policy implication, as the study provides no support for earlier claims of irrational trading behaviour by participants in world commodity markets.
Re-examines the notion that the prices of unrelated commodities move together, using a measure of co-movement of economic time series called concordance.
This paper provides a comprehensive analysis of the degree of co-movement among the nominal price returns of 11 major energy, agricultural, and food commodities using monthly data between 1970 and 2013. The authors study the extent and the time evolution of unconditional and conditional correlations using a uniform-spacings testing approach, a multivariate dynamic conditional correlation model and a rolling regression procedure.
Recent economic growth in China and other Asian countries has led to increased commodity demand which has caused price rises and accompanying price fluctuations not only for crude oil but also for the many other raw materials. Such trends mean that world commodity markets are once again under intense scrutiny. This book provides new insights into the modeling and forecasting of primary commodity prices by featuring comprehensive applications of the most recent methods of statistical time series analysis. The latter utilize econometric methods concerned with structural breaks, unobserved components, chaotic discovery, long memory, heteroskedasticity, wavelet estimation and fractional integration. Relevant tests employed include neural networks, correlation dimensions, Lyapunov exponents, fractional integration and rescaled range. The price forecasting involves structural time series trend plus cycle and cyclical trend models. Practical applications focus on the price behaviour of more than twenty international commodity markets.
The author estimates the effects on growth of commodity price shocks, and uncertainty within an established empirical growth model. Ex-post shocks, and ex-ante uncertainty have been treated in the empirical literature as if they were synonymous. But they are distinct concepts, and it is both theoretically, and empirically inappropriate to treat them as synonymous. He shows that the interaction between policy, and aid is robust to the inclusion of variables capturing commodity price movements. More important, his approach departs in three ways from earlier empirical studies of the subject: 1) It deals with issues of endogeneity, without incurring an excessive loss of efficiency. 2) It defines the dependent variable to allow an assessment of the longer-term implications of temporary trade shocks. 3) It imposes no priors on how commodity price movements affect growth, but compares and contrasts a range of competing shock, and uncertainty specifications. The author resolves the disagreement about the long-run effect of positive shocks on growth, finding that positive shocks have no long-run impact on growth (that windfalls from trade shocks do not translate into sustainable increases in income). He shows that negative shocks have large, highly significant, and negative effects on growth, but that commodity price uncertainty does not affect growth.
This book examines the issue of price volatility in agricultural commodities markets and how this phenomenon has evolved in recent years. The factors underlying the price spike of 2007-08 appear to be global and macroeconomic in nature, including the rapid growth in demand by developing countries, the international financial crisis, and exchange rate movements. Some of these factors are new, appearing as influences on price volatility only in the last decade. Although volatility has always been a feature of agricultural commodity markets, the evidence suggests that volatility has increased in certain commodity markets. A growing problem is that agricultural price shocks and volatility disrupt agricultural markets, economic incentives and incomes. With increased globalization and integration of financial and energy markets with agricultural commodity markets, the relationships between markets are expanding and becoming more complex. When a crisis such as a regional drought, food safety scare or a financial crisis hits a particular market, policy-makers often do not know the extent to which it will impact on other markets and affect producer, consumer and trader decisions. Including contributions from experts at the World Bank, the Food and Agriculture Organization of the United Nations, the USDA, and the European Commission, the research developed throughout the chapters of this book is based on current methodologies that can be used to analyze price volatility and provide directions for understanding this volatility and the development of new agricultural policies. The book highlights the challenges facing policy makers in dealing with the changing nature of agricultural commodities markets, and offers recommendations for anticipating price movements and managing their consequences. It will be a practical guide for both present and future policy-makers in deciding on potential price-stabilizing interventions, and will also serve as a useful resource for researchers and students in agricultural economics.
The authors examine the price linkages among polyester (the dominant chemical fiber), cotton (the dominant natural fiber), and crude oil (the dominant energy commodity), based on monthly data between 1980 and 2002. The modeling framework incorporates several aspects of the unit root econometrics literature. They find that: a) There is strong co-movement between cotton and polyester prices, well above the co-movement observed between these two prices and prices of other primary commodities. b) Crude oil prices have a stronger effect on polyester prices compared with cotton prices. c) Price shocks originating in the polyester market are transmitted at much higher speed to the cotton market than vice-versa.